Quantitative ultrasonic characterization of diffuse scatterers in the presence of structures that produce coherent echoes

Abstract

Quantitative ultrasound (QUS) techniques that parameterize the backscattered power spectrum have demonstrated significant promise for ultrasonic tissue characterization. Some QUS parameters, such as the effective scatterer diameter (ESD), require the assumption that the examined medium contains uniform diffuse scatterers. Structures that invalidate this assumption can significantly affect the estimated QUS parameters and decrease performance when classifying disease. In this work, a method was developed to reduce the effects of echoes that invalidate the assumption of diffuse scattering. To accomplish this task, backscattered signal sections containing non-diffuse echoes were identified and removed from the QUS analysis. Parameters estimated from the generalized spectrum (GS) and the Rayleigh SNR parameter were compared for detecting data blocks with non-diffuse echoes. Simulations and experiments were used to evaluate the effectiveness of the method. Experiments consisted of estimating QUS parameters from spontaneous fibroadenomas in rats and from beef liver samples. Results indicated that the method was able to significantly reduce or eliminate the effects of non-diffuse echoes that might exist in the backscattered signal. For example, the average reduction in the relative standard deviation of ESD estimates from simulation, rat fibroadenomas, and beef liver samples were 13%, 30%, and 51%, respectively. The Rayleigh SNR parameter performed best at detecting non-diffuse echoes for the purpose of removing and reducing ESD bias and variance. The method provides a means to improve the diagnostic capabilities of QUS techniques by allowing separate analysis of diffuse and non-diffuse scatterers.

Scatterer Number Density Considerations in Reference Phantom-Based Attenuation Estimation

“…We noted that small differences in envelope SNR had a large effect on the attenuation estimation variance once the SNR began to significantly deviate from 1.91. Ex vivo porcine (Bush et al, 1993; Zhou et al, 2013), bovine (Bevan and Sherar, 2001a, 2001b; Dewall et al, 2010; Gertner et al, 1997; Luchies et al, 2012, 2012; Parker, 1983; Pereira and Maciel, 2001), rat (Kemmerer and Oelze, 2012), and human (Machado et al, 2006) liver have all been used for the development of ultrasonic tissue characterization algorithms based on features extracted from RF data. However, we are not aware of any studies comparing estimates of envelope statistics in excised liver with estimates of envelope statistics in an in vivo setting.…”

Backscatter coefficient estimation using tapers with gaps

“…The solution to the minimization problem in Equation (15) is given by the eigenvector corresponding to the largest eigenvalue of the matrix given by 29 (16) for m, n = 1, 2, …, N. The MB taper with gaps is the eigenvector corresponding to the largest eigenvalue for the matrix given by 17 (17) Examples of MB tapers with gaps are shown in Figure 2.…”

Mean scatterer spacing estimation using multi-taper coherence

“…To date in ultrasound, the most commonly employed estimator for the GS is a synchronized, time-averaged estimate [15], [28], [30], [32]: $$\begin{array}{c}left{\hat{\gamma }}_{\text{WB},i}(f_1,f_2)=\frac{1}{N_{\text{S}}}\sum _{i=1}^{N_{\text{S}}}{Y}_{i}false(f_{1}false){\phi }_{i}false(f_{1}false)Y_i^{\ast }false(f_{2}false){\phi }_i^{\ast }false(f_{2}false)\\ {\gamma }_{\text{WB}}(f_1,f_2)=\frac{1}{N_{\text{A}}}\sum _{i=1}^{N_{\text{A}}}{\widehat{\gamma }}_{\text{WB},i}false(f_1,f_{2}false).\end{array}$$…”